| BrainBERT: Self-supervised representation learning for intracranial recordings C Wang, V Subramaniam, AU Yaari, G Kreiman, B Katz, I Cases, A Barbu ICLR, 2023 | 35 | 2023 |
| Revealing Vision-Language Integration in the Brain with Multimodal Networks V Subramaniam, C Conwell, C Wang, G Kreiman, B Katz, I Cases, ... ICML, 2024 | 8* | 2024 |
| Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains V Subramaniam, Y Du, JB Tenenbaum, A Torralba, S Li, I Mordatch ICLR, 2025 | 3 | 2025 |
| Population Transformer: Learning population-level representations of neural activity G Chau, C Wang, S Talukder, V Subramaniam, S Soedarmadji, Y Yue, ... ICLR, 2024 | 3 | 2024 |
| Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli C Wang, AU Yaari, AK Singh, V Subramaniam, D Rosenfarb, J DeWitt, ... NeurIPS, 2024 | 1 | 2024 |
| Modeling typological frequency with a grammatical learner V Subramaniam, A Albright OCP, 2019 | 1 | 2019 |
| Training the Untrainable: Introducing Inductive Bias via Representational Alignment V Subramaniam, D Mayo, C Conwell, T Poggio, B Katz, B Cheung, ... arXiv preprint arXiv:2410.20035, 2024 | | 2024 |
| Workshop Submission: Towards Making Untrainable Networks Trainable V Subramaniam, TA Poggio, B Katz, B Cheung, A Barbu UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural …, 2024 | | 2024 |
| Connecting Deep Learning Models to the Human Brain V Subramaniam Massachusetts Institute of Technology, 2024 | | 2024 |
| Workshop Submission: Using Multimodal DNNs to Study Vision-Language Integration in the Brain V Subramaniam, C Conwell, C Wang, G Kreiman, B Katz, I Cases, ... ICLR 2023 Workshop on Multimodal Representation Learning: Perks and Pitfalls, 2023 | | 2023 |
| Incorporating Typological Information into Pretrained Word Representations for Improved Performance S Vighnesh Proceedings of the 2022 3rd International Conference on Control, Robotics …, 2022 | | 2022 |
